{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "autoscroll": "json-false",
    "collapsed": false,
    "ein.tags": [
     "worksheet-0"
    ]
   },
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "import sys \n",
    "from os import getcwd, path\n",
    "sys.path.append(path.dirname(getcwd()))\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sb\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "autoscroll": "json-false",
    "collapsed": false,
    "ein.tags": [
     "worksheet-0"
    ]
   },
   "outputs": [],
   "source": [
    "from utils import data\n",
    "from utils.paper import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from cohorts.functions import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "title_loc = \"left\"\n",
    "title_fontsize = 18\n",
    "title_kwargs = {\"loc\": title_loc, \"fontsize\": title_fontsize, \"fontweight\": \"bold\"}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def landscape():\n",
    "    from utils import data\n",
    "    from utils.paper import mann_whitney_hyper_label_printer, fishers_exact_hyper_label_printer\n",
    "    cohort = data.init_cohort()\n",
    "    cohort_df = cohort.as_dataframe(join_with=['pdl1'])\n",
    "    cohort_df['Response'] = cohort_df['benefit'].map(lambda v: 'Benefit' if v else 'No Benefit')\n",
    "    cohort_df['Benefit'] = cohort_df['benefit']\n",
    "    \n",
    "    from cohorts.functions import missense_snv_count, neoantigen_count\n",
    "    \n",
    "    cohort_df = cohort.as_dataframe(missense_snv_count)\n",
    "    \n",
    "    def load_effects_dataframe(self,\n",
    "                               patients=None, \n",
    "                               only_nonsynonymous=False):\n",
    "        all_effects = self.load_effects(patients=patients, \n",
    "                                        only_nonsynonymous=only_nonsynonymous\n",
    "                                        )\n",
    "        dfs = []\n",
    "        for (patient_id, effects) in all_effects.items():\n",
    "            df = effects.to_dataframe()\n",
    "            df['patient_id'] = patient_id\n",
    "            dfs.append(df)\n",
    "        import pandas as pd\n",
    "        effects_df = pd.concat(dfs, copy=False)\n",
    "        return effects_df\n",
    "\n",
    "    effects_df = load_effects_dataframe(cohort, only_nonsynonymous=True)\n",
    "    \n",
    "    from varcode.util import reverse_complement\n",
    "    def get_change(row):\n",
    "        ref = row['ref']\n",
    "        if ref == 'C' or ref == 'T':\n",
    "            return ref + '>' + row['alt']\n",
    "        else:\n",
    "            return reverse_complement(ref) + '>' + reverse_complement(row['alt'])\n",
    "\n",
    "\n",
    "    effects_df['Variant'] = effects_df.apply(get_change, axis=1)\n",
    "    \n",
    "    import seaborn as sb\n",
    "    import matplotlib.pyplot as plt\n",
    "    import math\n",
    "\n",
    "    def _bar_plot(data,\n",
    "                 title=None,\n",
    "                 figsize=None,\n",
    "                 colormap=None,\n",
    "                 ax=None,\n",
    "                 *args,\n",
    "                 **kwargs):\n",
    "        plot = data.plot(title=title,\n",
    "               kind='bar', \n",
    "               stacked=True, \n",
    "               figsize=figsize,\n",
    "               ax=ax,\n",
    "               colormap=colormap,\n",
    "               *args,\n",
    "               **kwargs\n",
    "            ).axes.get_xaxis().set_visible(False)\n",
    "        return plot\n",
    "\n",
    "    def _binned_bar_plot(data,\n",
    "                         cohort_df,\n",
    "                         sample_col,\n",
    "                         bin_by,\n",
    "                         sort_by,\n",
    "                         normalize=False,\n",
    "                         title=None,\n",
    "                         figsize=None,\n",
    "                         colormap=None,\n",
    "                         ax=None):\n",
    "\n",
    "        by_bin = data.groupby(\n",
    "            [sample_col, bin_by]\n",
    "        )[[sample_col]].count().unstack()\n",
    "\n",
    "        by_bin.columns = by_bin.columns.get_level_values(1)\n",
    "\n",
    "        if normalize:\n",
    "            by_bin = by_bin.div(by_bin.sum(axis=1), axis=0)\n",
    "\n",
    "        by_bin = by_bin.join(cohort_df.set_index(sample_col)[sort_by])\n",
    "        by_bin.sort_values(sort_by, inplace=True, ascending=False)\n",
    "        by_bin.drop(sort_by, axis=1, inplace=True)\n",
    "\n",
    "        p = _bar_plot(by_bin, \n",
    "                        title, \n",
    "                        figsize,\n",
    "                        colormap,\n",
    "                        ax,\n",
    "                        ylim=(0, 1) if normalize else None,\n",
    "                     )\n",
    "\n",
    "        plt.legend(title=bin_by, loc='center left', bbox_to_anchor=(1.0, 0.5))\n",
    "\n",
    "    def _indicator_plot(data,\n",
    "                       sample_col,\n",
    "                       indicator_col,\n",
    "                       colormap=None,\n",
    "                       figsize=None,\n",
    "                       ax=None):\n",
    "        indicator_data = data.set_index([sample_col])[[indicator_col]].T\n",
    "        indicator_plot = sb.heatmap(indicator_data,\n",
    "                   square=True,\n",
    "                   cbar=None,\n",
    "                   xticklabels=True,\n",
    "                   linewidths=1,\n",
    "                   cmap=colormap,\n",
    "                   ax=ax)\n",
    "        plt.setp(indicator_plot.axes.get_yticklabels(), rotation=0)\n",
    "        return indicator_plot\n",
    "\n",
    "    def landscape_plot(cohort,\n",
    "                       effects_df,\n",
    "                       neoantigens_df,\n",
    "                       sample_col,\n",
    "                       width=10,\n",
    "                       bar_height=4,\n",
    "                       neoantigens_bin_columns=[],\n",
    "                       effects_bin_columns=[],\n",
    "                       indicator_columns=[],\n",
    "                       value_columns=[],\n",
    "                       sort_by=missense_snv_count\n",
    "                      ):\n",
    "        \"\"\"Build a figure with a plot from each of the columns specified\n",
    "\n",
    "        Parameters\n",
    "        ----------\n",
    "        cohort : Cohort\n",
    "        effects_df : str\n",
    "            Description\n",
    "        sample_col : str\n",
    "            Column that represents a sample id\n",
    "        width : int, optional\n",
    "            Width of the full figure\n",
    "        bar_height : int, optional\n",
    "            Height of each bar plot\n",
    "        bin_columns : list, optional\n",
    "             Columns to build a binned/stacked bar plot from\n",
    "        indicator_columns : list, optional\n",
    "             Columns to build a indicator bar from \n",
    "        value_columns : list, optional\n",
    "            Columns to build a bar plot from\n",
    "        \"\"\"\n",
    "        cohort_size = len(cohort)\n",
    "        min_square_size = float(width) / (.75 * cohort_size)\n",
    "        num_bar_plots = len(neoantigens_bin_columns) + len(effects_bin_columns) + len(value_columns)\n",
    "        num_additional_plots = 1\n",
    "        additional_plot_ratio = 1.5\n",
    "        height = len(indicator_columns) * min_square_size + (num_bar_plots + \n",
    "                                                             num_additional_plots * additional_plot_ratio) * bar_height \n",
    "\n",
    "        grid_rows = int(math.ceil(float(height) / min_square_size)) + num_bar_plots + num_additional_plots\n",
    "        indicator_column_rows = len(indicator_columns)\n",
    "        \n",
    "        remaining_rows = int((grid_rows - indicator_column_rows - num_additional_plots - num_bar_plots))\n",
    "        #additional_rows = bar_rows * additional_plot_ratio\n",
    "        #bar_rows * num_bar_plots + additional_rows * num_additional_plots = remaining_rows\n",
    "        bar_rows = int(round(remaining_rows / (num_bar_plots + additional_plot_ratio * num_additional_plots)))\n",
    "        additional_rows = int(bar_rows * additional_plot_ratio)\n",
    "        \n",
    "        additional_rows = grid_rows - bar_rows\n",
    "        gridsize = (grid_rows, 2)\n",
    "\n",
    "        plt.figure(0, figsize=(width - 1, height))\n",
    "\n",
    "        current_row = 0\n",
    "\n",
    "        sort_col, sort_df = cohort.as_dataframe(sort_by, return_cols=True)\n",
    "        sort_df = sort_df[[sample_col, sort_col, 'benefit']]\n",
    "\n",
    "        is_first = True\n",
    "        for bin_by_col in effects_bin_columns:\n",
    "            ax = plt.subplot2grid(gridsize,\n",
    "                           (current_row, 0),\n",
    "                           colspan=2,\n",
    "                           rowspan=bar_rows)\n",
    "            if is_first:\n",
    "                ax.set_title(\"A\", **title_kwargs)\n",
    "            is_first = False\n",
    "\n",
    "            _binned_bar_plot(effects_df,\n",
    "                            sort_df,\n",
    "                            sample_col,\n",
    "                            bin_by_col,\n",
    "                            normalize=True if bin_by_col in ['Variant', 'VAF'] else False,\n",
    "                            ax=ax,\n",
    "                            sort_by=['benefit', sort_col])\n",
    "            current_row += bar_rows + 1\n",
    "\n",
    "        for bin_by_col in neoantigens_bin_columns:\n",
    "            ax = plt.subplot2grid(gridsize,\n",
    "                           (current_row, 0),\n",
    "                           colspan=2,\n",
    "                           rowspan=bar_rows)\n",
    "            _binned_bar_plot(neoantigens_df,\n",
    "                            sort_df,\n",
    "                            sample_col,\n",
    "                            bin_by_col,\n",
    "                            ax=ax,\n",
    "                            sort_by=['benefit', sort_col]\n",
    "                        )\n",
    "            current_row += bar_rows + 1\n",
    "\n",
    "        for on in value_columns:\n",
    "            ax = plt.subplot2grid(gridsize,\n",
    "                                  (current_row, 0),\n",
    "                                  colspan=2,\n",
    "                                  rowspan=bar_rows)\n",
    "            plot_col, df = cohort.as_dataframe(on)\n",
    "            df = df.merge(sort_df)\n",
    "            df.sort_values(['benefit', sort_col], inplace=True, ascending=False)\n",
    "            p = _bar_plot(df[plot_col],\n",
    "                      ax=ax,\n",
    "                      title=plot_col,)\n",
    "            current_row += bar_rows + 1\n",
    "\n",
    "            if 'PFS' in on:\n",
    "                plt.yticks(range(0, int(max(df[plot_col])), 6) )\n",
    "                labels = ['+' if c else '' for c in list(df['Censored'])]\n",
    "                rects = ax.patches\n",
    "                for rect, label in zip(rects, labels):\n",
    "                    height = rect.get_height()\n",
    "                    ax.text(rect.get_x() + rect.get_width()/2, height, label, ha='center', va='bottom')\n",
    "\n",
    "        current_row -= 1\n",
    "        cmaps = ['Greens', 'coolwarm', 'Blues', 'Greys',]\n",
    "        for (idx, indicator_on) in enumerate(indicator_columns):\n",
    "            ax = plt.subplot2grid(gridsize,\n",
    "                                  (current_row, 0),\n",
    "                                  colspan=2)\n",
    "            indicator_col, df = cohort.as_dataframe(indicator_on, return_cols=True)\n",
    "            df = df.merge(sort_df)\n",
    "            df.sort_values(['benefit', sort_col], inplace=True, ascending=False)\n",
    "            ip = _indicator_plot(df,\n",
    "                                sample_col,\n",
    "                                indicator_col,\n",
    "                                ax=ax,\n",
    "                                colormap=cmaps[idx]\n",
    "                                )\n",
    "            current_row += 1\n",
    "            ip.axes.xaxis.set_label(indicator_col.upper())\n",
    "            if idx != len(indicator_columns) - 1:\n",
    "                ip.axes.xaxis.set_visible(False)\n",
    "            else:\n",
    "                ip.axes.set_xlabel('Patient')\n",
    "                \n",
    "        current_row += 3\n",
    "        ax = plt.subplot2grid(gridsize,\n",
    "                              (current_row, 0),\n",
    "                               colspan=1,\n",
    "                               rowspan=additional_rows)\n",
    "        cohort.plot_benefit(on={\"# Missense SNVs / MB\": missense_snv_count}, ax=ax)\n",
    "        ax.set_title(\"B\", **title_kwargs)\n",
    "\n",
    "        ax = plt.subplot2grid(gridsize,\n",
    "                              (current_row, 1),\n",
    "                               colspan=1,\n",
    "                               rowspan=additional_rows)\n",
    "        cohort.plot_benefit(on={\"# Expr. Neoantigen / MB\": expressed_neoantigen_count}, ax=ax)\n",
    "        ax.set_title(\"C\", **title_kwargs)\n",
    "\n",
    "    from cohorts.variant_stats import variant_stats_from_variant\n",
    "    import pandas as pd\n",
    "    \n",
    "    variants = cohort.load_variants()\n",
    "    \n",
    "    effects_df.rename(columns = \n",
    "                      {'effect_type' : 'Effect Type',\n",
    "                       'is_transversion' : 'Transversion'\n",
    "                      }, inplace=True)\n",
    "\n",
    "    from varcode import Variant\n",
    "    def get_tumor_vaf(row):\n",
    "        p = row['patient_id']\n",
    "        v = Variant(row['contig'], row['start'], row['ref'], row['alt'], ensembl='grch37')\n",
    "        from cohorts.varcode_utils import FilterableVariant\n",
    "        fv = FilterableVariant(v, variants[p], p)\n",
    "        stats = variant_stats_from_variant(v, fv.variant_metadata)\n",
    "        return 100.0 * stats.tumor_stats.variant_allele_frequency\n",
    "\n",
    "    effects_df['Variant Allele Frequency'] = effects_df.apply(get_tumor_vaf, axis=1)\n",
    "    effects_df['VAF'] = pd.cut(effects_df['Variant Allele Frequency'], [0, 12.5, 25.0, 50.0, 100.0])\n",
    "    \n",
    "    def DCB(row):\n",
    "        return row['benefit']\n",
    "    \n",
    "    return landscape_plot(\n",
    "        cohort, \n",
    "        effects_df,\n",
    "        None,\n",
    "        width=(2250 / 150),\n",
    "        bar_height = 5, \n",
    "        sample_col = 'patient_id',\n",
    "        effects_bin_columns = ['Effect Type', 'VAF', 'Variant' ],\n",
    "        indicator_columns = [DCB] \n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from cohorts.styling import set_styling\n",
    "set_styling()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{{{figure_load}}}\n",
      "{'dataframe_hash': -2899676230513618006,\n",
      " 'provenance_file_summary': {u'cohorts': u'0.4.0+3.gda968fb',\n",
      "                             u'isovar': u'0.0.6',\n",
      "                             u'mhctools': u'0.3.0',\n",
      "                             u'numpy': u'1.11.1',\n",
      "                             u'pandas': u'0.18.1',\n",
      "                             u'pyensembl': u'1.0.3',\n",
      "                             u'scipy': u'0.18.1',\n",
      "                             u'topiary': u'0.1.0',\n",
      "                             u'varcode': u'0.5.10'}}\n",
      "inner join with pdl1: 25 to 25 rows\n",
      "inner join with ensembl_coverage: 25 to 25 rows\n",
      "Mann-Whitney test: U=94.0, p-value=0.223528962588 (two-sided)\n",
      "Mann-Whitney test: U=91.0, p-value=0.289681301627 (two-sided)\n"
     ]
    },
    {
     "data": {
      "image/png": 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XXiM+Pp633nqL3bt3A3Dffffx0UcfsXv3boqKipg2bRotW7bEx8fnH/XRsWNH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\nAAAAE/0fmGoBPikg9RMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8b88685750>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hyper_figure_label_printer(\"figure_load\")\n",
    "landscape()\n",
    "fig = plt.gcf()\n",
    "fig.savefig(path.join(data.REPO_DATA_DIR, 'Fig2.tif'), format='tif', dpi=300, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from utils.paper import resize_image_plos\n",
    "resize_image_plos(\"Fig2.tif\", \"Fig2.tif\", figure_path=data.REPO_DATA_DIR)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11"
  },
  "name": "TCR Clonality.ipynb"
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
